Inferring critical thresholds of ecosystem transitions from spatial data

Area of Science:

• Ecology
• Dynamical Systems Theory
• Remote Sensing

Background:

• Ecosystems can exhibit alternative stable states and undergo abrupt transitions when critical thresholds are crossed.
• Dynamical systems theory predicts critical slowing down as an early warning signal, but it lacks quantitative threshold estimation.
• Existing methods for threshold quantification are limited in scope or data requirements.

Purpose of the Study:

• To develop and validate a novel method for estimating critical thresholds of ecosystem transitions using spatial data.
• To demonstrate the applicability of this method to real-world ecosystem data.
• To provide a more quantitative approach to early warning signals for ecosystem shifts.

Main Methods:

• Investigated cellular-automaton models of ecosystem dynamics transitioning between high-density and bare states.
• Computed spatial variance and autocorrelation of ecosystem state variables along driver gradients.
• Applied the method to remotely sensed vegetation data (Enhanced Vegetation Index, EVI) along rainfall gradients in Africa and Australia.

Main Results:

• Spatial variance and autocorrelation of ecosystem state variables reliably estimate critical thresholds in modeled ecosystems.
• Analysis of EVI data in Africa and Australia yielded critical threshold estimates consistent with independent methods.
• Maximum spatial variance and autocorrelation indicate critical thresholds in ecosystem state and driver values.

Conclusions:

• Spatial metrics (variance and autocorrelation) provide a robust method for estimating critical thresholds in ecosystems prone to alternative stable states.
• This method offers a significant advancement in quantifying early warning signals for abrupt ecosystem changes.
• The approach is broadly applicable across diverse ecosystems exhibiting alternative stable states and can be implemented with spatial data.